The need for innovative systemic approaches for managing water resources that integrate natural and human dimensions is well established. Although systemic, participatory modelling has been shown to support stakeholder involvement and integrated analysis, the uptake within acrimonious, data-scarce contexts – especially in the developing world – is limited. This study details a process designed to address challenges facing the lower Olifants River Catchment in South Africa, including deteriorating water quality, data paucity and stakeholder conflict. Narratives and quantitative data were used to build a system dynamics (SD) model, ResiMod, within a participatory process. The paper demonstrates how narratives can inform, and be informed by, iterative model development whilst integrating scientific data. The approach facilitated an exploration of perceptions of causality, connections between stakeholder sectors, and mitigatory actions for responding to climate-change impacts on biodiversity. This offers a promising approach to support improved communication and learning in disputed, data-scarce contexts.

Holistic water quality models to support decision-making in lowland catchments with competing stakeholder perspectives are still limited. To address this gap, an integrated system dynamics model for water quantity and quality (including stream temperature, dissolved oxygen, and macronutrients) was developed. Adaptable plug-n-play modules handle the complexity (sources, pathways) related to both urban and agricultural/natural land-use features. The model was applied in a data-rich catchment to uncover key insights into the dynamics governing water quality in a peri-urban stream. Performance indicators demonstrate the model successfully captured key water quantity/quality variations and interactions (with, e.g., Nash-Sutcliff Efficiency ranging from very good to satisfactory). Model simulation and sensitivity results could then highlight the influence of stream temperature variations and enhanced heterotrophic respiration in summer, causing low dissolved oxygen levels and potentially affecting ecological quality. Probabilistic uncertainty results combined with a rich dataset show high potential for ammonium uptake in the macrophyte-dominated reach. The results further suggest phosphorus remobilization from streambed sediment could become an important diffuse nutrient source should other sources (e.g., urban effluents) be mitigated. These findings are especially important for the design of green transition solutions, where single-objective management strategies may negatively impact aquatic ecosystems.

Dynamic Line Rating (DLR) is a smart grid technology that allows the rating of electrical conductors to be increased based on local weather conditions. Overhead lines are conventionally given a conservative rating based on worst case scenarios. We demonstrate that observing the conditions in real time leads to additional capacity and safer operation. This paper provides a report of a pioneering scheme in the United States of America in which DLR has been applied. Thereby, we demonstrate that observing the local weather conditions in real time leads to additional capacity and safer operation. Secondly, we discuss limitations involved. In doing so, we arrive at novel insights which will inform and improve future DLR projects. Third, we provide a policy background and discussion to clarify the technology's potential and identifies barriers to the imminent adoption of dynamic line rating systems. We provide suggestions for regulatory bodies about possible improvements in policy to encourage adoption of this beneficial technology.

Marginal cost curves (MCCs) are popular decision-support tools for assessing and ranking the cost-effectiveness of different options in environmental policy and management. However, conventional MCC approaches have been criticized for lack of transparency and disregard for complexity; not accounting for interaction effects between measures; ignoring ancillary benefits and costs; and not considering intertemporal dynamics. In this paper, we present an approach to address these challenges using a system dynamics (SD)-based model for producing dynamic MCCs. We describe the approach by applying it to evaluate efforts to address water scarcity in a hypothetical, but representative, Swedish city. Our results show that the approach effectively addresses all four documented limitations of conventional MCC methods. They also show that combining MCCs with behavior-over-time graphs and causal-loop diagrams can lead to new policy insights and support a more inclusive decision-making process.

This systematic review examines the intersection of systems thinking and learning theory in addressing rural–urban challenges in light of increasing global urbanization. We explore how different dimensions of systems thinking—ontological (how we understand systems) and epistemological (how we think about systems)—align with single-, double-, and triple-loop learning in rural–urban research from 2014 to 2024. Through a rigorous screening process of the peer-reviewed literature, we analyze how theoretical frameworks manifest in research approaches, methodological choices, and learning outcomes. Our findings reveal promising developments and persistent gaps in current approaches, and suggest pathways for more integrated theoretical and methodological frameworks. We also highlight the need for studies that develop knowledge and practices that support collective learning and joint trajectories towards sustainability from a cross-sectorial perspective in rural–urban geographies. This synthesis contributes to discussions on how to effectively address complex challenges at the rural–urban interface while advancing both theoretical understanding and practical application.